Forum Discussion
Best Implementation Practices
Hi experts, I have recently join a Fabric greenfield project,my previous experience has been building up data lake ,delta lake and datalakehouse. The client has smaller user base of less than 100 use...
v-sathmakuri
Community Support
Community SupportHi SamyAbdul ,
Thank you for reaching out to fabric community.
For your scenario, OneLake + Lakehouse is a suitable choice if the workload is mainly API-based analytics rather than transactional OLTP.
Key things to define upfront:
- Architecture: Bronze -> Silver -> Gold -> Semantic Model.
- Workspace: Keep Dev/Test/Prod separate, but avoid unnecessary workspaces.
- API ingestion: Plan for incremental loads, pagination, retries, 429/rate limits, and schema changes.
- Security: Use least-privilege access, service principals/managed identities and RLS where required.
- Performance: Avoid small files and unnecessary full loads.
- Monitoring: Maintain audit tables for pipeline status, record counts, watermarks and failures.
- CI/CD: Establish Git, deployment and environment parameterization early.
- Governance: Define naming, ownership, retention and data-quality standards.
Thanks!!
Thank you v-sathmakuri since the user-base of less than 100 users, and client is focused on costs, we are trying to start with F16 capacity to begin with, if needed later on we can move to F64, and My earlier topology (Ingest, Data, BI × 3 environments) gives 9 workspaces. For under 100 users and one team, two per environment (Data engineering and BI) is enough, so 6 in total. Add domain-specific workspaces only when NetSuite, iMIS and HubSpot arrive. Are these steps in the right direction? thanks again
